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http://papers.nips.cc/paper/3202-parallelizing-support-vector-machines-on-distributed-computers.pdf
PSVM: Parallelizing Support Vector Machines on Distributed Computers Edward Y. Chang⁄, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, & Hang Cui Google Research, Beijing, China Abstract Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability,
https://www.researchgate.net/publication/221620344_PSVM_Parallelizing_Support_Vector_Machines_on_Distributed_Computers
Request PDF PSVM: Parallelizing Support Vector Machines on Distributed Computers Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and ...
https://link.springer.com/chapter/10.1007/978-3-642-20429-6_10
Aug 26, 2011 · Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to perform parallel computation.Cited by: 228
https://github.com/openbigdatagroup/psvm
Mar 03, 2016 · If you wish to publish any work based on psvm, please cite our paper as: Edward Chang, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, and Hang Cui, PSVM: Parallelizing Support Vector Machines on Distributed Computers.
http://ai.deepq.com/psvm/
Why PSVM. Although widely used, Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. PSVM achieves memory reduction and computation speedup via a row-based parallel Incomplete Cholesky Factorization (ICF) algorithm and parallel Interior-Point Method(IPM). Empirical study shows that PSVM …
https://www.bartleby.com/essay/Support-Vector-Machines-On-Distributed-Computers-F3R9A9L2LMWW
May 05, 2016 · PSVM: Parallelizing Support Vector Machines on Distributed Computers Edward Y. Chang∗, Kaihua Zhu, Hao Wang, Hongjie Bai, Jian Li, Zhihuan Qiu, & Hang Cui Google Research, Beijing, China Abstract Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time.
https://papers.nips.cc/paper/3202-parallelizing-support-vector-machines-on-distributed-computers
Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to perform parallel computation.
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